We are sharing a specialised part-time consulting opportunity for experienced software engineers with strong open-source contributions and demonstrable GitHub or GitLab profiles to contribute to an advanced AI training and software engineering evaluation project.
Selected professionals will create reproducible reinforcement-learning environments designed to test advanced AI systems on realistic software engineering problems involving bug fixing, feature implementation, codebase refactoring, and performance optimisation. The work requires strong hands-on engineering expertise, high-quality public code contributions, and the ability to develop rigorous reference solutions and clearly document technical reasoning. No prior experience in AI is required.
Key Responsibilities
Software Engineering & Open-Source Contribution
- Contribute expert-level code samples and development solutions in Python3, Java, Rust, Go, C++, TypeScript, or comparable languages
- Apply experience gained from real-world open-source or production codebases
- Implement robust functionality while considering scalability, maintainability, and software quality
- Demonstrate sound software engineering judgement across complex development tasks
- Produce solutions that can be independently reproduced and validated
Debugging & Feature Development
- Analyse, troubleshoot, and resolve complex software defects across diverse codebases
- Identify root causes of incorrect behaviour, regressions, and system failures
- Implement new features from requirements through validated delivery
- Diagnose and resolve performance bottlenecks and inefficient implementation patterns
- Address edge cases and ensure solutions remain reliable across relevant scenarios
Refactoring & Performance Optimisation
- Refactor legacy or complex code to improve clarity, maintainability, and long-term reliability
- Identify architectural or implementation weaknesses within existing systems
- Improve software performance while preserving functional correctness
- Evaluate trade-offs between development speed, scalability, complexity, and maintainability
- Modernise codebases where appropriate without introducing unnecessary regressions
AI Evaluation Environments & Reference Solutions
- Create reinforcement-learning environments that evaluate AI systems on realistic software engineering tasks
- Develop reproducible problem environments and corresponding golden reference solutions
- Design tasks involving bug fixing, feature implementation, codebase refactoring, and optimisation
- Document technical reasoning, implementation decisions, and verification methodology clearly
- Review and validate peer-contributed code and technical submissions for correctness and clarity
Ideal Profile
- Clear and demonstrable open-source contributions through GitHub, GitLab, or comparable public development profiles
- Significant hands-on expertise in at least one of Python3, Java, Rust, Go, C++, or TypeScript
- Deep understanding of algorithms, data structures, and software engineering fundamentals
- Proven ability to debug complex systems and resolve technically challenging software defects
- Strong experience implementing robust software features
- Experience with performance optimisation and technical bottleneck analysis
- Background in large-codebase refactoring or legacy-system modernisation is advantageous
- Track record of delivering meaningful technical contributions from conception through implementation
- Strong ability to reason about code correctness, maintainability, and system behaviour
- Excellent technical documentation and communication skills
- Ability to review other engineers' code and identify technical weaknesses precisely
- Interest in AI systems and technical evaluation is beneficial
- No prior experience in AI training is required
Engagement Details
- Part-time independent contractor engagement
- Fully remote
- Compensation: $50–$100/hour
- Expected commitment: approximately 15 hours per week
- Compensation is output-based, with payment made for tasks that meet project specifications
- Minimum weekly submission requirements apply
- Applicants must be able to demonstrate meaningful open-source contributions through a public GitHub, GitLab, or comparable profile
- Work will involve software engineering, reinforcement-learning environment creation, debugging, feature development, refactoring, optimisation, and reference-solution development
- The selection process may include screening questions, an approximately 30-minute AI interview, a technical assessment, and hiring-manager review
- Selected professionals should be prepared to begin their first tasks within approximately 24–48 hours of completing onboarding
- Roles are typically filled within approximately 48 hours
- Project scope, workload, task complexity, and evaluation standards may evolve depending on project requirements
- Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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